Multi-Scale Coal Fire Detection Based on an Improved Active Contour Model from Landsat-8 Satellite and UAV Images

Underground coal fires can increase surface temperature, cause surface cracks and collapse, and release poisonous and harmful gases, which significantly harm the ecological environment and humans. Traditional methods of extracting coal fires, such as global threshold, K-mean and active contour model, usually produce many false alarms. Therefore, this paper proposes an improved active contour model by introducing the distinguishing energies of coal fires and others into the traditional active contour model. Taking Urumqi, Xinjiang, China as the research area, coal fires are detected from Landsat-8 satellite and unmanned aerial vehicle (UAV) data. The results show that the proposed method can eliminate many false alarms compared with some traditional methods, and achieve detection of small-area coal fires by referring field survey data. More importantly, the results obtained from UAV data can help identify not only burning coal fires but also potential underground coal fires. This paper provides an efficient method for high-precision coal fire detection and strong technical support for reducing environmental pollution and coal energy use.

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Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang

Remote Sensing ◽

10.3390/rs13061141 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1141

Author(s):

Jinglong Liu ◽

Yunjia Wang ◽

Shiyong Yan ◽

Feng Zhao ◽

Yi Li ◽

...

Keyword(s):

Remote Sensing ◽

Identification Accuracy ◽

Omission Error ◽

Detection Accuracy ◽

Landsat 8 ◽

Commission Error ◽

Thermal Anomalies ◽

Coal Fire ◽

Coal Fires ◽

Underground Coal Fires

Underground coal fires have become a worldwide disaster, which brings serious environmental pollution and massive energy waste. Xinjiang is one of the regions that is seriously affected by the underground coal fires. After years of extinguishing, the underground coal fire areas in Xinjiang have not been significantly reduced yet. To extinguish underground coal fires, it is critical to identify and monitor them. Recently, remote sensing technologies have been showing great potential in coal fires’ identification and monitoring. The thermal infrared technology is usually used to detect thermal anomalies in coal fire areas, and the Differential Synthetic Aperture Radar Interferometry (DInSAR) technology for the detection of coal fires related to ground subsidence. However, non-coal fire thermal anomalies caused by ground objects with low specific heat capacity, and surface subsidence caused by mining and crustal activities have seriously affected the detection accuracy of coal fire areas. To improve coal fires’ detection accuracy by using remote sensing technologies, this study firstly obtains temperature, normalized difference vegetation index (NDVI), and subsidence information based on Landsat8 and Sentinel-1 data, respectively. Then, a multi-source information strength and weakness constraint method (SWCM) is proposed for coal fire identification and analysis. The results show that the proposed SWCM has the highest coal fire identification accuracy among the employed methods. Moreover, it can significantly reduce the commission and omission error caused by non-coal fire-related thermal anomalies and subsidence. Specifically, the commission error is reduced by 70.4% on average, and the omission error is reduced by 30.6%. Based on the results, the spatio-temporal change characteristics of the coal fire areas have been obtained. In addition, it is found that there is a significant negative correlation between the time-series temperature and the subsidence rate of the coal fire areas (R2 reaches 0.82), which indicates the feasibility of using both temperature and subsidence to identify and monitor underground coal fires.

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A Novel Image Segmentation Algorithm Based on Active Contour Model and Retinex Model

Proceedings of the 2019 3rd International Conference on Advances in Image Processing ◽

10.1145/3373419.3373451 ◽

2019 ◽

Author(s):

Jin Liu ◽

Jianqiao Wang ◽

Qi Li ◽

Miaohua Shi

Keyword(s):

Image Segmentation ◽

Active Contour ◽

Active Contour Model ◽

Segmentation Algorithm ◽

Contour Model ◽

Image Segmentation Algorithm

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Active Contour Model with Adaptive weighted function for Robust Image Segmentation under Biased Conditions

Expert Systems with Applications ◽

10.1016/j.eswa.2021.114811 ◽

2021 ◽

pp. 114811

Author(s):

Aditi Joshi ◽

Mohammed Saquib Khan ◽

Asim Niaz ◽

Farhan Akram ◽

Hyun Chul Song ◽

...

Keyword(s):

Image Segmentation ◽

Active Contour ◽

Active Contour Model ◽

Weighted Function ◽

Contour Model ◽

Robust Image

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Automatic segmentation of medical images using a novel Harris Hawk optimization method and an active contour model

Journal of X-Ray Science and Technology ◽

10.3233/xst-210879 ◽

2021 ◽

pp. 1-19

Author(s):

Maria Tamoor ◽

Irfan Younas

Keyword(s):

Image Segmentation ◽

Active Contour ◽

Medical Image ◽

Active Contour Model ◽

Heart Diseases ◽

Medical Image Segmentation ◽

Gaussian Kernel ◽

Initial Contour ◽

Automated Method ◽

Contour Model

Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of a segmentation method is important for further treatments of different diseases. Different medical imaging modalities have different challenges such as intensity inhomogeneity, noise, low contrast, and ill-defined boundaries, which make automated segmentation a difficult task. To handle these issues, we propose a new fully automated method for medical image segmentation, which utilizes the advantages of thresholding and an active contour model. In this study, a Harris Hawks optimizer is applied to determine the optimal thresholding value, which is used to obtain the initial contour for segmentation. The obtained contour is further refined by using a spatially varying Gaussian kernel in the active contour model. The proposed method is then validated using a standard skin dataset (ISBI 2016), which consists of variable-sized lesions and different challenging artifacts, and a standard cardiac magnetic resonance dataset (ACDC, MICCAI 2017) with a wide spectrum of normal hearts, congenital heart diseases, and cardiac dysfunction. Experimental results show that the proposed method can effectively segment the region of interest and produce superior segmentation results for skin (overall Dice Score 0.90) and cardiac dataset (overall Dice Score 0.93), as compared to other state-of-the-art algorithms.

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Active Contour Model Using Fast Fourier Transformation for Salient Object Detection

Electronics ◽

10.3390/electronics10020192 ◽

2021 ◽

Vol 10 (2) ◽

pp. 192

Author(s):

Umer Sadiq Khan ◽

Xingjun Zhang ◽

Yuanqi Su

Keyword(s):

Object Detection ◽

Fourier Transformation ◽

Active Contour ◽

Active Contour Model ◽

Salient Object Detection ◽

Fast Fourier Transformation ◽

Salient Object ◽

Active Contour Models ◽

Contour Model ◽

Contour Models

The active contour model is a comprehensive research technique used for salient object detection. Most active contour models of saliency detection are developed in the context of natural scenes, and their role with synthetic and medical images is not well investigated. Existing active contour models perform efficiently in many complexities but facing challenges on synthetic and medical images due to the limited time like, precise automatic fitted contour and expensive initialization computational cost. Our intention is detecting automatic boundary of the object without re-initialization which further in evolution drive to extract salient object. For this, we propose a simple novel derivative of a numerical solution scheme, using fast Fourier transformation (FFT) in active contour (Snake) differential equations that has two major enhancements, namely it completely avoids the approximation of expansive spatial derivatives finite differences, and the regularization scheme can be generally extended more. Second, FFT is significantly faster compared to the traditional solution in spatial domain. Finally, this model practiced Fourier-force function to fit curves naturally and extract salient objects from the background. Compared with the state-of-the-art methods, the proposed method achieves at least a 3% increase of accuracy on three diverse set of images. Moreover, it runs very fast, and the average running time of the proposed methods is about one twelfth of the baseline.

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